Biological signal processing device, watching system, and watching method

ABSTRACT

An object is to provide a biological signal processing device capable of evaluating the reliability of the measurement state of a biological signal, based on RRI information. A biological signal processing device includes: an RRI information acquisition means for acquiring RRI information composed of RRIs, of a biological signal measured by a sensor, arranged in time series, from the sensor; a map generation means for plotting points whose positions are determined based on values of the RRIs constituting the RRI information, on a feature space, and generating a map from the feature space; an index calculation means for calculating an index indicating resemblance to heart rate variability of each RRI constituting RRI information to be evaluated; and a reliability calculation means for calculating RRI reliability of each RRI constituting the RRI information, from the index.

TECHNICAL FIELD

The present disclosure relates to a biological signal processing device,a watching system, and a watching method.

BACKGROUND ART

Conventionally, there have been watching systems that centrally managethe physical and mental states of workers in factories and the like,drivers who are driving vehicles, elderly people who live alone, etc.Some of these systems determine the state of a target to be watched,from a biological signal. In such a watching system, the reliability ofa biological signal measured by a sensor is important. Conventionally,there has been a watching system that extracts R waves from a cardiacpotential waveform, and evaluates the reliability of the measurementstate of an instantaneous heart rate (RRI: R-R-interval), which is theinterval between two R waves adjacent to each other in time series,according to the type of the measurement state of the two R waves (see,for example, Patent Document 1). In the technology described in PatentDocument 1, the measurement state of each extracted R wave is determinedon the basis of the potential information of each R wave, therebyevaluating the reliability of an RRI.

CITATION LIST Patent Document

-   Patent Document 1: Japanese Laid-Open Patent Publication No.    2018-094156

SUMMARY OF THE INVENTION Problems to be Solved by the Invention

The technology described in Patent Document 1 requires not only RRIinformation but also information about the shape of each R wave.However, among sensors that measure biological signals, many sensorsoutput only RRI information and do not output a cardiac potentialwaveform itself. In the case where such a sensor is used in theabove-described watching system, it is necessary to evaluate thereliability of the measurement state of a biological signal from onlyRRI information, but the technology described in Patent Document 1cannot handle this evaluation.

The present disclosure has been made to solve the above problem, and anobject of the present disclosure is to provide a biological signalprocessing device that is capable of evaluating the reliability of themeasurement state of a biological signal on the basis of RRIinformation.

Another object of the present disclosure is to provide a watching systemand a watching method that are capable of evaluating the reliability ofthe measurement state of a biological signal on the basis of RRIinformation and are capable of grasping the state of a target to bewatched, from the RRI information.

Solution to the Problems

A biological signal processing device according to the presentdisclosure includes: an RRI information acquisition means for acquiring,from a sensor for calculating RRIs of a biological signal of a target tobe watched, RRI information composed of a plurality of the RRIs arrangedin time series; a map generation means for plotting a plurality of firstpoints whose positions are determined on the basis of values of the RRIsin a normal state among the RRIs constituting the RRI information, on afeature space, and generating a map from the feature space on the basisof the plurality of first points; an index calculation means forplotting a second point whose position is determined by a value of eachRRI to be evaluated, on the feature space, and calculating an indexindicating resemblance to heart rate variability of each RRI to beevaluated, on the basis of a relationship between the second point andthe map; and a reliability calculation means for calculating RRIreliability indicating reliability of each RRI to be evaluated, from theindex.

Moreover, a watching system according to the present disclosureincludes: a sensor for calculating RRIs of a biological signal of atarget to be watched; an RRI information acquisition means for acquiringRRI information composed of a plurality of the RRIs arranged in timeseries; a map generation means for plotting a plurality of first pointswhose positions are determined on the basis of values of the RRIs in anormal state among the RRIs constituting the RRI information, on afeature space, and generating a map from the feature space on the basisof the plurality of first points; an index calculation means forplotting a second point whose position is determined by a value of eachRRI to be evaluated, on the feature space, and calculating an indexindicating resemblance to heart rate variability of each RRI to beevaluated, on the basis of a relationship between the second point andthe map; a reliability calculation means for calculating RRI reliabilityindicating reliability of each RRI to be evaluated, from the index; anRRI information correction means for correcting the RRI information onthe basis of the RRI reliability and outputting the RRI information ascorrection RRI information; a heart rate variability analysis means foranalyzing heart rate variability of the target to be watched, on thebasis of the correction RRI information; and an analysis result outputmeans for outputting an analysis result by the heart rate variabilityanalysis means.

Moreover, a watching method according to the present disclosureincludes: a step of acquiring RRIs of a biological signal of a target tobe watched, by a sensor, and acquiring RRI information composed of aplurality of the RRIs arranged in time series; a step of plotting aplurality of first points whose positions are determined on the basis ofvalues of the RRIs in a normal state among the plurality of the RRIs, ona feature space, and generating a map from the feature space on thebasis of the plurality of first points; a step of plotting a secondpoint whose position is determined by a value of each RRI to beevaluated, on the feature space, and calculating an index indicatingresemblance to heart rate variability of each RRI to be evaluated, onthe basis of a relationship between the second point and the map; a stepof calculating RRI reliability indicating reliability of each RRI to beevaluated, from the index; a step of correcting the RRI information onthe basis of the RRI reliability to obtain correction RRI information; astep of analyzing heart rate variability of the target to be watched, onthe basis of the correction RRI information; and a step of outputting ananalysis result by the step of analyzing heart rate variability.

Effect of the Invention

The biological signal processing device according to the presentdisclosure is capable of evaluating the reliability of the measurementstate of a biological signal on the basis of RRI information.

Moreover, the watching system according to the present disclosure iscapable of evaluating the reliability of the measurement state of abiological signal on the basis of RRI information and is capable ofgrasping the state of a target to be watched, from the RRI information.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a general cardiac potential waveform.

FIG. 1B shows a general pulse waveform.

FIG. 2A shows a cardiac potential waveform in the case where omission ofdetection of an R wave occurs.

FIG. 2B shows a cardiac potential waveform in the case where erroneousdetection of R waves occurs.

FIG. 3 illustrates the characteristics of general RRI information.

FIG. 4 is a block diagram showing a biological signal processing deviceaccording to Embodiment 1.

FIG. 5 illustrates an index calculation method according to Embodiment1.

FIG. 6 illustrates an example of the hardware configuration of thebiological signal processing device according to Embodiment 1.

FIG. 7A is a block diagram showing a watching system according toEmbodiment 1.

FIG. 7B is a block diagram showing a watching server according toEmbodiment 1.

FIG. 8 is a flowchart showing the operation of the watching systemaccording to Embodiment 1.

FIG. 9A is a block diagram showing a watching system according toanother example of Embodiment 1.

FIG. 9B is a block diagram showing a watching server according toanother example of Embodiment 1.

FIG. 10 illustrates a map and an index calculation method according toEmbodiment 2.

FIG. 11 is a block diagram showing a biological signal processing deviceaccording to Embodiment 3.

FIG. 12 illustrates a map and an index calculation method according toEmbodiment 3.

FIG. 13 is a flowchart showing the operation of a watching systemaccording to Embodiment 3.

FIG. 14 illustrates a map and an index calculation method according toEmbodiment 4.

FIG. 15 shows the relationship between an angle θ*, which is the vertexangle of an isosceles triangle indicating a normal region according toEmbodiment 4, and the number of points P_(k)* inside the normal region.

DESCRIPTION OF EMBODIMENTS Embodiment 1

(Description of RRI Information)

Embodiment 1 will be described with reference to FIG. 1A to FIG. 9B. The“target to be watched” in Embodiment 1 is a person who can be monitoredor watched, such as a worker who performs work in a factory or the like,a driver who is driving a vehicle, and an elderly person who livesalone. First, RRI information treated in the present disclosure will bedescribed with reference to FIG. 1A and FIG. 1B. FIG. 1A shows a generalcardiac potential waveform, the horizontal axis represents time, and thevertical axis represents potential. In a cardiac potential waveform V1shown in FIG. 1A, sharp peaks appear at intervals. These peak portionsV2 are generally referred to as R waves. R waves are signal changeswhich occur reflecting ventricular excitation, and the interval betweenR waves adjacent to each other in time series is defined as an RRI. TheRRI represents the time required for a single beat of the heart.Usually, R waves are extracted by peak detection, and the time intervalsbetween the sequentially measured R waves are RRIs. The RRIs can beobtained as a series of time-sequence data from a cardiac potentialwaveform. In FIG. 1A, an RRI obtained at the nth time is denoted asRRI(n), and an RRI obtained at the n+1th time is denoted as RRI(n+1).Whereas a heart rate represents the number of heart beats per minute,the reciprocal of an RRI may be used as an instantaneous heart rate tofinely grasp the variation of an exercise load. In addition, theappearance interval of R waves fluctuates under the control of theautonomic nerves. Therefore, the state of the autonomic nerves may beestimated by analyzing the temporal variation of the RRIs.

As shown in FIG. 1B, similar to the cardiac potential waveform, a pulsewave W repeats a similar waveform every beat. Therefore, similar to thecardiac potential waveform, the equivalent of an RRI can be calculatedfor the pulse wave. For example, the interval between peaks adjacent toeach other or the interval between valleys adjacent to each other can betreated in the same manner as an RRI. In FIG. 1B, the interval isdenoted as RRI* to be distinguished from the RRI in the cardiacpotential waveform. In addition, a threshold value Wth is set as shownby a dotted line in FIG. 1B, and it is also possible to treat theinterval between timings of exceeding the threshold value Wth in thesame manner as an RRI. The RRI in this case is represented as RRI** inFIG. 1B. In the following, the RRI in the cardiac potential waveform V1will be described, but unless otherwise specified, the same applies tothe RRI* and the RRI** in the pulse wave W.

(Omission of Detection and Erroneous Detection of R Waves)

Omission of detection and erroneous detection of R waves in the case ofusing a cardiac potential waveform will be described with reference toFIG. 2A and FIG. 2B. In the case of acquiring a cardiac potentialwaveform or a pulse wave using a contact type sensor, signal detectionmay become unstable due to violent body movements or an abnormality inwearing of the sensor, resulting in loss of data of the cardiacpotential waveform or mixing of noise in the cardiac potential waveform.For example, V3 shown in FIG. 2A is an R wave, but has a small peakpotential and cannot be detected as an R wave. Therefore, in the cardiacpotential waveform shown in FIG. 2A, omission of detection of an R waveoccurs. In addition, noise V4 shown in FIG. 2B is not an R wave but isdetected as an R wave. Therefore, in the cardiac potential waveformshown in FIG. 2B, erroneous detection of R waves occurs. In FIG. 2A andFIG. 2B, the RRI that should be measured is indicated by a dotted arrow,and the RRI that is actually measured is indicated by a solid arrow, andFIG. 2A and FIG. 2B show that RRIs are not accurately measured.

(Characteristics of RRI Information)

The characteristics of RRI information will be described with referenceto FIG. 3 . It is assumed that RRI information is represented asindicated by the following equation (1) from RRIs obtained in timeseries during a certain period.

$\begin{matrix}\left\lbrack {{Math}.1} \right\rbrack &  \\{{RRI} = \left\{ {{{RRI}(1)},{{RRI}(2)},{\ldots{{RRI}(i)}},{{RRI}\left( {i + 1} \right)},{\ldots{{RRI}(N)}}} \right\}} & (1)\end{matrix}$ $\begin{matrix}\begin{matrix}{P_{1} = \left( {{{RRI}(1)},{{RRI}(2)}} \right)^{T}} \\{P_{2} = \left( {{{RRI}(2)},{{RRI}(3)}} \right)^{T}}\end{matrix} & (2)\end{matrix}$ P_(N − 1) = (RRI(N − 1), RRI(N))^(T) $\begin{matrix}{P = \left( {P_{1},P_{2},P_{3},{\ldots P_{i}},{\ldots P_{N - 1}}} \right)} & (3)\end{matrix}$

FIG. 3 is a plot of each point P_(i) (i=1, 2, . . . N−1) constituting apoint group P, on an xy plane, and illustrates the characteristics ofgeneral RRI information. That is, if the state of a target to be watchedis normal during the period in which the RRIs constituting each pointP_(i) are acquired, a cardiac potential waveform is stationary, so thatthe RRIs are almost constant. As a result, each point P_(i) is plottedin the vicinity of a straight line L0 which is a straight line of y=x,and the point group P is generated in the vicinity of the straight lineL0. The distribution region of the point group P, that is, thedistribution region of the respective points P_(i) constituting thepoint group P, is formed in an elliptical shape and intersects thestraight line L0. Here, the straight line L0 is a straight lineindicating a state where each RRI constituting the RRI information isconstant, on the xy plane.

(Description of Biological Signal Processing Device)

FIG. 4 is a block diagram showing a biological signal processing deviceaccording to Embodiment 1. A biological signal processing device 10includes: an RRI information acquisition means 11 which acquires RRIinformation 82 outputted from a sensor 80; a map generation means 12which generates a “map” for evaluating the reliability of the RRIinformation 82; a map storage means 13 which stores therein mapinformation 84 of the map generated by the map generation means; anindex calculation means 14 which calculates an index 83 of each RRIconstituting the RRI information 82 to be evaluated, by using the map; areliability calculation means 15 which calculates RRI reliability 85,which indicates the degree of reliability of each RRI constituting theRRI information 82 to be evaluated, from the index 83; and an RRIinformation correction means 16 which corrects the RRI information 82 onthe basis of the RRI reliability 85 and outputs the corrected RRIinformation 82 as correction RRI information 86.

The sensor 80 calculates RRIs of a target to be watched, and outputs theRRI information 82 composed of the measured RRIs arranged in timeseries. In addition, when outputting the RRI information 82, the sensor80 adds identification information (not shown) which identifies thetarget to be watched, to the RRI information 82. The identificationinformation is also added to the RRI reliability 85 and the correctionRRI information 86, which will be described later, and the RRIinformation 82, the RRI reliability 85, and the correction RRIinformation 86 are associated with the target to be watched. The sensor80 may be any sensor that is capable of detecting a biological signalsuch as cardiac potential or a pulse wave and calculating an RRI fromthe detected biological signal. For example, a wearable sensor includingan electrode that detects cardiac potential or an optical element thatdetects a pulse wave, or the like, can be used as the sensor 80. Anon-contact type pulse wave sensor that detects a pulse wave from bloodflow on the surface of a face can also be used.

The RRI information acquisition means 11 acquires the RRI information 82periodically outputted from the sensor 80, during a predeterminedperiod. The acquisition of the RRI information 82 in Embodiment 1includes “acquisition in pre-learning” and “acquisition in actualoperation” (described in detail later). The RRI information acquisitionmeans 11 transmits RRI information 821 acquired through “acquisition inpre-learning”, to the map generation means 12, and transmits RRIinformation 822 acquired through “acquisition in actual operation”, tothe index calculation means 14.

(Description of Map Generation Method)

The map generation means 12 generates a map from the RRI information 821acquired in pre-learning. For the sake of description, the RRIinformation 821 is assumed to include M RRIs. That is, the RRIinformation 821 is information obtained by replacing N with M inequation (1). “M” is a predetermined number. The map generation means 12plots M−1 points P_(i) (i=1, 2, . . . M−1), that is, first points, onthe xy plane according to equations obtained by replacing N with M inequation (2) and equation (3), to generate the point group P. The pointgroup P generated thus intersects the straight line L0 (y=x) as shown inFIG. 3 . In Embodiment 1, one in which the point group P is generated onthe xy plane which is a feature space is defined as a “map”. The “map”is used for evaluating the resemblance to heart rate variability of theRRI information 82 to evaluate the reliability of the RRI information82. Therefore, the position of each point P_(i) constituting the pointgroup P for generating a map needs to be determined on the basis of thevalues of RRIs in a normal state. For this reason, when generating thepoint group P by pre-learning, the influence of data loss and noise iseliminated as much as possible to maintain the normal state.Specifically, pre-learning is performed with the sensor worn normally.In addition, while maintaining the above state, it is preferable toacquire as much RRI information 821 in various situations assumed duringactual operation as possible, and generate a map. The map generationmeans 12 outputs information for reproducing the generated map, as themap information 84 to the map storage means 13. The map storage means 13stores the map information 84 therein.

The map information 84 is information required for reproducing the mapgenerated by the map generation means 12, in the feature space. In thecase where a map is generated on the basis of the point group P as inEmbodiment 1, the map information 84 includes the coordinates of all thepoints P_(i) constituting the point group P. In Embodiment 1, asdescribed above, the point group P is configured through pre-learning,and a map is generated on the basis of the point group P, so that themap is generated before the start of actual operation. Therefore, aprocess from later-described index calculation to RRI correction can beperformed in real time in actual operation.

(Description of Index Calculation Method)

The index calculation means 14 calculates the index 83 of each RRIconstituting the RRI information 822 to be evaluated that is acquired inactual operation. Here, the index 83 indicates the “resemblance to heartrate variability” of each RRI constituting the RRI information 822. FIG.5 illustrates an index calculation method according to Embodiment 1.First, the index calculation means 14 acquires the map information 84from the map storage means 13 and configures the point group P on the xyplane by using the map information 84, thereby reproducing the map.Next, the index calculation means 14 applies equation (2) to each RRI tobe evaluated that constitutes the RRI information 822, and plots a pointcorresponding to the value of each RRI to be evaluated, on the xy plane.Here, it is assumed that N RRIs are acquired and the plotted points arepoints P_(k)* (k=1, 2, . . . N−1). The points P_(k)* correspond tosecond points. The index calculation means 14 calculates a degree ofdeviation of each point P_(k)* from the point group P as a value beforecalculating an index. As for a specific example of the “degree ofdeviation”, it is considered that a smallest distance d between thepoint P_(k)* and the point P_(i) among the distances between the pointP_(k)* and the respective points P_(i) constituting the point group P isdefined as the degree of deviation of the point P_(k)*. After the degreeof deviation of each point P_(k)* is calculated, the index calculationmeans 14 calculates the index 83 of each RRI which is an element of thepoint P_(k)*, from the degree of deviation of the point P_(k)*. Fromequation (2), for example, an RRI(k) is an element of a point P_(k-1)*and also an element of the point P_(k)*, so that each RRI may beelements of two points P_(k)*. Therefore, it is considered that thesimple average of the reciprocal of the degree of deviation of the pointP_(k-1)* and the reciprocal of the degree of deviation of the pointP_(k)* is defined as the index 83 of the RRI(k). Accordingly, the closerthe point P_(k)* is to the point group P, the smaller the index 83 isand the larger the resemblance to heart rate variability is. After theindex 83 of each RRI to be evaluated is calculated, the indexcalculation means 14 outputs the index 83 of each RRI to the reliabilitycalculation means 15.

As for the degree of deviation of the point P_(k)*, distances d of aplurality of points P_(i) around the point P_(k)* may be calculated, anda predetermined number of (for example, 10) distances d from the smallerones are added up to obtain a total value, and this total value may beused as the degree of deviation. In addition, the index 83 of each RRIis not limited to the simple average of the reciprocals of the degreesof deviation as described above, and may be the reciprocal of the sum ofthe degrees of deviation or may be the reciprocal of the maximum valueor the minimum value among the degrees of deviation.

(Description of RRI Reliability Calculation)

For each RRI to be evaluated, the reliability calculation means 15calculates the RRI reliability 85 on the basis of the index 83 of eachRRI. The RRI reliability 85 is an index indicating the reliability ofthe measurement state of the biological signal, and is represented as afunction of the index 83. That is, when the value of the index 83 of theRRI(k) is denoted by α(k) and the value of the RRI reliability 85 of theRRI(k) is denoted by β(k), the following equation (4) is generallyestablished.

β(k)=f(α(k))  (4)

Here, α(k) indicates the degree of resemblance to heart ratevariability, and β(k) indicates the reliability of the RRI(k), so thatthe function f is generally considered to be a monotonically increasingfunction. As a simplest form, the index 83 may be used as the RRIreliability 85 of the RRI. In addition, the function f is alsoconsidered to be a nonlinear monotonically increasing function such as asigmoid function.

Moreover, the RRI reliability 85 may be calculated on the basis of theindexes 83 of the RRIs in certain previous and subsequent sections. Thatis, the RRI reliability 85 of the RRI(k) may be obtained by using thefollowing equation (5).

β(k)=f(α(k−j),α(k−j+1) . . . ,α(k−1),α(k),α(k+1), . . .α(k+j−1),α(k+j))  (5)

In equation (5), the RRI reliability 85 of the RRI(k) is obtained on thebasis of the indexes 83 of the RRIs in a section [k−j, k+j]. Morespecifically, the average of the indexes 83 of the RRIs in the section[k−j, k+j] is considered to be the RRI reliability of the RRI(k).

The reliability calculation means 15 outputs the RRI reliability 85 ofeach RRI to the RRI information correction means 16. In addition, thereliability calculation means 15 outputs the RRI reliability 85 of eachRRI as the output of the biological signal processing device 10 to awatching server 70 described later. The output of the RRI reliability 85to the watching server 70 can be omitted in the case wherelater-described determination as to the worn state of the sensor 80 isnot performed.

(Description of RRI Information Correction)

The RRI information correction means 16 compares the value of the RRIreliability 85 of each RRI with a predetermined threshold value th, andwhen the value of the RRI reliability 85 is smaller than the thresholdvalue th, the RRI information correction means 16 invalidates thecorresponding RRI. That is, the RRI information correction means 16invalidates the RRI(k) in the case of β(k)<th. In addition, the RRIinformation correction means 16 corrects the RRI information 822, whichis the RRI information to be evaluated, by deleting the invalidated RRIfrom the RRI information 822. The corrected RRI information 822 isoutputted as the correction RRI information 86.

The biological signal processing device 10 of Embodiment 1 corrects theRRI information, but it is also considered that the biological signalprocessing device 10 performs up to calculation of the RRI reliability85. In this case, the biological signal processing device 10 serves as adevice for evaluating the reliability of the RRI information 82, andoutputs the RRI reliability 85.

Moreover, considering that certain arrhythmias can occur even in normalconditions, only when the value of the RRI reliability 85 consecutivelyfalls below the threshold value th a predetermined number of times, thecorresponding RRI may be invalidated. For example, in the case where theabove predetermined number of times is set to 2, if β(k−1)<th and β(k),or if β(k)<th and β(k+1), the RRI(k) may be invalidated. Accordingly,RRIs in arrhythmias that can occur in normal conditions can be left inthe correction RRI information 86, and accidental deletion of normalRRIs can be prevented.

(Description of Hardware Configuration)

Next, the hardware configuration for implementing the function units ofthe biological signal processing device 10 will be described.

FIG. 6 illustrates an example of the hardware configuration forimplementing the function units of the biological signal processingdevice 10. The biological signal processing device 10 is composed mainlyof a processor 91, a memory 92 as a main storage device, and anauxiliary storage device 93. The processor 91 is composed of, forexample, a central processing unit (CPU), an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), or the like. The memory 92 is composedof a volatile storage device such as a random access memory, and theauxiliary storage device 93 is composed of a nonvolatile storage devicesuch as a flash memory, a hard disk, or the like. In the auxiliarystorage device 93, a predetermined program to be executed by theprocessor 91 is stored. The processor 91 reads and executes this programas appropriate to perform various arithmetic processes. At this time,the predetermined program is temporarily stored in the memory 92 fromthe auxiliary storage device 93, and the processor 91 reads this programfrom the memory 92. The arithmetic processes by the function units shownin FIG. 4 are realized by the processor 91 executing the predeterminedprogram as described above. The results of the arithmetic processes bythe processor 91 are stored in the memory 92 once, and are stored in theauxiliary storage device 93 according to the purposes of the executedarithmetic processes.

Moreover, the biological signal processing device 10 includes areception circuit 94 which receives the RRI information 82 outputted bythe sensor 80, and a transmission circuit 95 which transmits the RRIreliability 85 and the correction RRI information 86 outputted by thebiological signal processing device 10, to an external device.

(Description of Watching System)

FIG. 7A is a block diagram showing a watching system according toEmbodiment 1, and FIG. 7B is a block diagram showing a watching serveraccording to Embodiment 1. A watching system 100 includes: sensors 80Aand 80B each of which detects a biological signal of a target to bewatched, such as cardiac potential or a pulse wave, sequentiallycalculates RRIs of the detected biological signal, and outputs RRIinformation 82A or 82B composed of a plurality of the RRIs arranged intime series; biological signal processing devices 10A and 10B whichcorrespond to the sensors 80A and 80B, respectively, and each of whichprocesses the RRI information 82A or 82B outputted by the sensor 80A or80B to generate RRI reliability 85A or 85B and correction RRIinformation 86A or 86B; and the watching server 70 which receives theRRI reliability 85 and the correction RRI information 86 outputted byeach of the biological signal processing devices 10A and 10B, analyzesthe correction RRI information 86, and outputs an analysis result E. Theconfigurations of the biological signal processing devices 10A and 10Bare the same as that of the biological signal processing device 10described with reference to FIG. 4 . That is, a map is generated fromthe RRIs constituting the RRI information 82A or 82B acquired from thesensor 80A or the sensor 80B, the index 83 indicating the resemblance toheart rate variability of each RRI to be evaluated is calculated, andthe reliability of each RRI is calculated by using the index 83, therebyobtaining the RRI reliability 85A or 85B. In addition, the RRIinformation 82A or 82B is corrected by the RRI reliability 85A or 85B toobtain the correction RRI information 86A or 86B. The specificdescription of the map generation, the calculation of the index 83, thecalculation of the RRI reliability, and the correction of the RRIinformation 82 is as described above.

(Description of Watching Server)

The watching server 70 includes: a reception means 71 which receives theRRI reliability 85A and 85B and the correction RRI information 86A and86B; a heart rate variability analysis means 72 which analyzes the heartrate variability of targets to be watched on the basis of the correctionRRI information 86A and 86B, respectively, and estimates the physicalloads, the states of the autonomic nerves, and the like of the targetsto be watched; and an analysis result output means 73 which outputs aresult of the analysis by the heart rate variability analysis means 72,as the analysis result E.

(Description of Heart Rate Variability Analysis)

The heart rate variability analysis means 72 analyzes the heart ratevariability of each target to be watched from the time-series variationof each RRI constituting the correction RRI information 86A or 86B. Forexample, the activation balance between the parasympathetic nerves andthe sympathetic nerves may be evaluated from the intensity distributionof respiratory arrhythmia components to estimate the state of theautonomic nerves, thereby evaluating the intensity of stress on thetarget to be watched. In addition, the variation of work load orexercise load and the variation of the RRIs are compared with eachother, and the physical load of the target to be watched is estimated.Specifically, the physical load of the target to be watched is estimatedfrom the variation of the RRIs with respect to the variation of workload or the like (the heart rate increases when the work load or thelike increases, and the heart rate also returns when the work load orthe like returns to normal). The variation of work load or the like canbe grasped, for example, by measuring the movement of the target to bewatched with an acceleration sensor or the like and determining whetherthere is any deviation in the measured acceleration. The heart ratevariability analysis means 72 outputs the analysis result E to theanalysis result output means 73.

As described above, in Embodiment 1, the RRIs whose RRI reliabilityvalues are smaller than the threshold value th are invalidated and arenot included in the correction RRI information 86A and 86B. Therefore,the above-described analysis of heart rate variability can be performedin a state where the influence of an abnormality in wearing of thesensors 80A and 80B is eliminated.

Prior to analysis of heart rate variability, the heart rate variabilityanalysis means 72 may determine whether there is an abnormality in theworn state of the sensor 80A or 80B worn by the target to be watched.Specifically, for each RRI constituting the received correction RRIinformation 86A or 86B, if a state where the value of the RRIreliability falls below the threshold value th continues for apredetermined period, the heart rate variability analysis means 72determines that there is an abnormality in the worn state of the sensor80A or 80B. In such a case, the heart rate variability analysis means 72adds a warning message indicating the wearing abnormality, to theanalysis result E.

The analysis result output means 73 outputs the analysis result E to anexternal display device or the like to display the contents of theanalysis result E to a supervisor or the like.

In the example shown in FIG. 7A, one biological signal processing devicecorresponds to one sensor, but one biological signal processing devicemay correspond to a plurality of sensors. In this case, the biologicalsignal processing device processes RRI information outputted by each ofthe corresponding sensors, and outputs RRI reliability and correctionRRI information for each RRI information.

(Description of Watching Method)

Next, operation will be described. FIG. 8 is a flowchart showing theoperation of the watching system according to Embodiment 1, that is, awatching method according to Embodiment 1. In FIG. 8 , step ST101 andstep ST102 are a pre-learning process, and step ST103 to step ST108 arean actual operation process. First, RRI information for learning isacquired (step ST101). The sensors 80A and 80B each detect a biologicalsignal, in normal conditions, of the target to be watched, and calculateRRIs of the detected biological signal in time series. The RRIinformation acquisition means 11 of the biological signal processingdevices 10A and 10B each acquire the RRI information 82A or 82B composedof a plurality of the RRIs arranged in time series, from the sensor 80Aor 80B.

Next, a map is generated from the RRI information in normal conditionsacquired in step ST101 (step ST102). The map generation means 12 of thebiological signal processing devices 10A and 10B each configure a pointgroup P as a map on the xy plane by using the RRI information 821.Specific generation of the point group P is as described above. The mapgeneration means 12 stores the coordinates of all points P_(i)constituting the point group P, as the map information 84 in the mapstorage means 13. Thus, the pre-learning process is completed.

Next, RRI information of the target to be watched is acquired in actualoperation (step ST103). The RRI information acquisition means 11 of thebiological signal processing devices 10A and 10B acquires the RRIinformation 82A and 82B periodically outputted from the sensors 80A and80B which detect biological signals of the targets to be watched. TheRRI information 82A and 82B acquired by the biological signal processingdevices 10A and 10B, respectively, are outputted as the RRI information822 to be evaluated, to the index calculation means 14 of the biologicalsignal processing devices 10A and 10B.

Next, the index of each RRI to be evaluated is calculated (step ST104).As described above, the index calculation means 14 of the biologicalsignal processing devices 10A and 10B each calculate the index 83 ofeach RRI from the degree of deviation between the point P_(k)*corresponding to each RRI to be evaluated that constitutes the RRIinformation 822 and the point group P generated in pre-learning.

Next, RRI reliability and correction RRI information are calculated(step ST105). The reliability calculation means 15 of the biologicalsignal processing devices 10A and 10B calculates the RRI reliability 85Aand 85B from the index 83 of each RRI calculated in step ST104, andoutputs the RRI reliability 85A and 85B to the RRI informationcorrection means 16 of the biological signal processing devices 10A and10B and the watching server 70. The RRI information correction means 16of the biological signal processing devices 10A and 10B corrects the RRIinformation 822 to be evaluated, on the basis of the RRI reliability 85Aand 85B to obtain the correction RRI information 86A and 86B. A specificmethod for correcting the RRI information is as described above. The RRIinformation correction means 16 of the biological signal processingdevices 10A and 10B outputs the correction RRI information 86A and 86Bto the watching server 70.

Next, the worn state of the sensor 80 is determined (step ST106). Thewatching server 70 receives the RRI reliability 85A and 85B and thecorrection RRI information 86A and 86B outputted from the biologicalsignal processing devices 10A and 10B, by the reception means 71, andthe heart rate variability analysis means 72 of the watching server 70compares the values of the RRI reliability 85A and 85B with thethreshold value th, thereby determining the worn states of the sensors80A and 80B, respectively.

Next, the heart rate variability of the target to be watched is analyzed(step ST107). The heart rate variability analysis means 72 estimates thestates of the autonomic nerves and the physical loads of the targets tobe watched, from the time-series variation of the RRIs constituting thecorrection RRI information 86A and 86B. A specific analysis method is asdescribed above.

Next, the analysis result is displayed (step ST108). The analysis resultoutput means 73 of the watching server 70 outputs the analysis result Eby the heart rate variability analysis means 72, to an external displaydevice or the like to display the contents of the analysis result E to asupervisor or the like. At this time, if there is information to warnthe supervisor of, display of a warning message, output of a warningsound, or the like is performed. The supervisor performs confirmation ofthe worn state of the sensor on the target to be watched, confirmationof the safety of the target to be watched, or the like, according to thecontents of the analysis result E and the warning.

Next, another example of the watching system according to Embodiment 1will be described. FIG. 9A is a block diagram showing a watching systemaccording to another example of Embodiment 1, and FIG. 9B is a blockdiagram showing a watching server according to the other example ofEmbodiment 1. A watching system 101 includes: sensors 80A and 80B eachof which detects a biological signal of a target to be watched,calculates RRIs of the detected biological signal in time series, andoutputs RRI information 82A or 82B composed of a plurality of the RRIsarranged in time series; and a watching server 701 which acquires theRRI information 82A and 82B outputted by the sensors 80A and 80B, andoutputs a result obtained by analyzing the acquired RRI information 82Aand 82B, as an analysis result E.

The watching server 701 includes: a reception means 711 which receivesthe RRI information 82A and 82B; a biological signal processing means715 which generates RRI reliability 85A and 85B and correction RRIinformation 86A and 86B from the RRI information 82A and 82B; a heartrate variability analysis means 72 which analyzes the heart ratevariability of targets to be watched on the basis of the correction RRIinformation 86A and 86B, respectively, and estimates the physical loads,the states of the autonomic nerves, and the like of the targets to bewatched; and an analysis result output means 73 which outputs a resultof the analysis by the heart rate variability analysis means 72, as ananalysis result E. The configuration of the biological signal processingmeans 715 is the same as that of the biological signal processing device10. In addition, the other configuration is also the same as that of thewatching server 70. As described above, the watching server 701 isconfigured by incorporating the function of the biological signalprocessing device 10 into the watching server 70, to directly acquirethe RRI information 82A and 82B from the sensors 80A and 80B and performcorrection of RRI information, etc., within the watching server.

As can be seen by comparing the watching system 100 and the watchingsystem 101, it is possible to transfer some functions of the biologicalsignal processing device 10 to the watching server 70. In short, it issufficient that in the watching system 100, acquisition of RRIinformation, correction of the RRI information, analysis of heart ratevariability, and output of an analysis result can be performed. Forexample, it is also considered that the heart rate variability analysismeans 72 is provided in the biological signal processing device 10. Inthis case, offline analysis of heart rate variability is possible, andthe analysis result E is transmitted from the biological signalprocessing device 10 to the watching server 70. The watching server 70merely performs display of the analysis result E to a supervisor, etc.

Moreover, although not shown, it is also considered that a map storagemeans which stores therein map information and a transmission meanswhich transmits the map information are provided in the watching server70, a map generated by the biological signal processing device 10A isstored in the map storage means of the watching server, and the map isshared by the biological signal processing devices 10A and 10B. In thiscase, the pre-learning process can be omitted if map information of anavailable map is already stored in the watching server 70.

In Embodiment 1, a two-dimensional plane is used as the feature space,but the feature space may be a three or more dimensional space. In thecase where the feature space is three-dimensional, the coordinates ofeach point P_(i) constituting the point group P is composed of threeadjacent RRIs (for example, P_(i)=(RRI(i−1), RRI(i), RRI(i+1))^(T)). Inthe case where the feature space is an xyz space, if RRIs arestationary, the points P_(i) constituting the point group P aredistributed in the vicinity of a straight line x=y=z, so that the index83 and the RRI reliability 85 of each RRI can be calculated in the samemanner as in the case where the feature space is two-dimensional.

The biological signal processing device of Embodiment 1 can evaluate thereliability of the measurement state of the biological signal on thebasis of the RRI information. More specifically, the biological signalprocessing device includes: the map generation means which generates amap from the feature space by plotting a plurality of points whosepositions are determined on the basis of the values of the RRIs in thenormal state among the RRIs of the biological signal of the target to bewatched, on the feature space; the index calculation means whichcalculates an index indicating the resemblance to heart rate variabilityof each RRI to be evaluated, by comparing each point whose position isdetermined on the basis of the value of the RRI to be evaluated, withthe map; and the reliability calculation means which calculates RRIreliability indicating the reliability of each RRI to be evaluated, fromthe index. Accordingly, a map serving as a basis for evaluation isgenerated in pre-learning, and the reliability of the value of eachmeasured RRI of the target to be watched is evaluated from only the RRIinformation in actual operation. Therefore, for the target to bewatched, the reliability of the measurement state of the biologicalsignal can be evaluated on the basis of the RRI information.

Moreover, since the RRI information correction means which corrects theRRI information by deleting the RRI whose RRI reliability falls belowthe predetermined threshold value, from the RRI information, isincluded, the influence of a wearing abnormality on heart ratevariability analysis can be suppressed.

Moreover, the watching system of Embodiment 1 can evaluate thereliability of the measurement state of the biological signal on thebasis of the RRI information, and grasp the state of the target to bewatched from the RRI information. More specifically, the watching systemincludes: the sensor which calculates RRIs of the biological signal ofthe target to be watched; the biological signal processing device ofEmbodiment 1 which acquires the RRI information from the correspondingsensor; and the watching server including the heart rate variabilityanalysis means which analyzes the heart rate variability of the targetto be watched, on the basis of the correction RRI information receivedfrom the signal processing device of Embodiment 1. The biological signalprocessing device of Embodiment 1 can evaluate the reliability of themeasurement state of the biological signal on the basis of the RRIinformation as described above. Furthermore, analysis of heart ratevariability can be performed on the basis of the RRI information by theheart rate variability analysis means in the watching server, so thatthe state of the target to be watched can be grasped from the RRIinformation.

Moreover, the heart rate variability analysis means evaluates the wornstate of the sensor on the basis of the RRI reliability, so that awarning indicating an abnormality in wearing of the sensor can be issuedto the supervisor.

Embodiment 2

Next, Embodiment 2 will be described with reference to FIG. 10 .Embodiment 2 is different from Embodiment 1 in a method for generating amap for evaluating the reliability of RRI information and a method forcalculating the index of an RRI. FIG. 10 illustrates a map and an indexcalculation method according to Embodiment 2. In Embodiment 2, a regiondetermined so as to include a point group obtained in pre-learning isset as a normal region, and in actual operation, on the basis of whethera point plotted by using each RRI to be evaluated is within the normalregion or outside the normal region, an index of the RRI is determined.Hereinafter, a detailed description will be given.

(Description of Map Generation Method)

First, the map generation means 12 configures a point group P from theRRI information 821 acquired for pre-learning. As for the generation ofthe point group P, similar to Embodiment 1, equation (2) and equation(3) may be applied to, for example, M RRIs. Similar to Embodiment 1, thepoint group P intersects a straight line L0, and the distribution rangeof points P_(i) is formed in an elliptical shape.

Next, a straight line L01 which passes through a point Pa locatedfarthest from a straight line L0 (y=x) among the points P_(i)constituting the point group P and which intersects the straight line L0at an intersection point C0, is drawn. Here, it is assumed that an anglebetween the straight line L0 and the straight line L01 is θmin. Next, anangle θ (=θmin+Δθ) obtained by adding a margin Δθ to θmin is calculated,and a straight line L1 which intersects the straight line L0 at theintersection point C0 and whose angle with respect to the straight lineL0 is θ, is drawn. Furthermore, a straight line L2 which is symmetricalwith the straight line L1 about the straight line L0 as an axis ofsymmetry, is drawn. In addition, a straight line L3 which has a gradientof −1 and which intersects the straight line L1 and the straight line L2at an intersection point C1 and an intersection point C2, respectively,is drawn. A region surrounded by the straight line L1, the straight lineL2, and the straight line L3 is set as a normal region S. A straightline L02 in the drawing is a straight line that is symmetrical with thestraight line L01 about the straight line L0 as an axis of symmetry.Therefore, an angle between the straight line L02 and the straight lineL0 is also θmin (note that the angle is not shown in FIG. 10 ).

Since θmin is the minimum angle for including all the points P_(i)constituting the point group P, all the points P_(i) constituting thepoint group P are included in the region surrounded by the straight lineL01, the straight line L02, and the straight line L3. Since the anglesof the straight line L1 and the straight line L2 with respect to thestraight line L0 are larger by the margin Δθ than those of the straightline L01 and the straight line L02, all the points P_(i) constitutingthe point group P are also included in the normal region S which is theregion surrounded by the straight line L1, the straight line L2, and thestraight line L3. In Embodiment 2, one in which the normal region S isset on an xy plane which is a feature space is defined as a “map”. Sincethe normal region S is determined by the straight lines L1, L2, and L3,the map information 84 in Embodiment 2 may include mathematical formulasindicating the straight lines L1, L2, and L3, which are the boundary ofthe normal region S, or coefficients determining the mathematicalformulas, etc. Therefore, it is not necessary to include coordinateinformation of all the points P_(i) constituting the point group P, inthe map information 84 as in Embodiment 1.

The positions of the intersection points C0, C1, and C2 are determinedin advance. It is considered that, on the basis of the physiologicalfindings, the positions of the intersection points C1 and C2 aredetermined from the possible maximum heartbeat interval value, and theposition of the intersection point C0 is determined from the possibleminimum heartbeat interval value. In the generation of a map inEmbodiment 1, it is necessary to increase the number of samples inpre-learning, and to configure a point group P after more exhaustivesampling. In Embodiment 2, the range of the map can be adjusted bysetting the intersection points on the basis of the physiologicalfindings, so that the map can be adjusted more flexibly than inEmbodiment 1. Thus, for example, even if only data in a state where theinstantaneous heart rate is low is obtained in pre-learning, a map thatalso assumes a state where the instantaneous heart rate is high can begenerated.

Moreover, the normal region S shown in FIG. 10 is an isosceles trianglethat has a vertex angle at the intersection point C0 and whose base is aline segment connecting the intersection point C1 and the intersectionpoint C2. However, the normal region S may be any region including allthe points P_(i) constituting the point group P on the boundary thereofor therein, and the geometric shape of the normal region S is notparticularly limited. For example, an ellipse or a diamond shape may beused. In addition, it is considered that a cone that includes all thepoints P_(i) constituting a point group P in the case where the featurespace is a three-dimensional xyz space, on the boundary thereof ortherein and that intersects a straight line x=y=z at the vertex and thebottom surface center thereof, is used as the normal region S.

(Description of Index Calculation Method)

The index calculation means 14 calculates the index 83 of each RRIconstituting the RRI information 822 to be evaluated that is acquired inactual operation. First, the index calculation means 14 acquires the mapinformation 84 from the map storage means 13, and sets the normal regionS on the xy plane by using the map information 84, thereby reproducingthe map. Next, the index calculation means 14 applies equation (2) toeach RRI to be evaluated that constitutes the RRI information 822, andplots a point corresponding to the value of each RRI to be evaluated, onthe xy plane. The index calculation means 14 determines whether or noteach point P_(k)* is within the normal region S, assigns “1” to thepoints P_(k)* that are within the normal region S, and assigns “0” tothe points P_(k)* that are outside the normal region S. After “0” or “1”is assigned to all the points P_(k)* as described above, the indexes 83of the RRIs constituting each point P_(k)* are calculated in the samemanner as in Embodiment 1. That is, the equivalent of a degree ofdeviation 831 of each point P_(k)* described in Embodiment 1 may beconsidered to be “0” or “1”. The index calculation means 14 outputs theindex 83 of each RRI to the reliability calculation means 15.

The others are the same as in Embodiment 1, and thus the descriptionthereof is omitted.

In Embodiment 2, “0” or “1” is assigned to the points P_(k)* in indexcalculation, and the numerical value to be assigned to each point P_(k)*is determined by the position of the point P_(k)*. That is, thenumerical value indicating the “resemblance to heart rate variability”is determined by a position on the xy plane. Using this fact, potentialinformation in which “1” is set for the inside of the normal region Sand “0” is set for the outside of the normal region S is set in mapgeneration, and one obtained by adding the potential information to thefeature space may be used as a map. In this case, the map information 84includes the above potential information. In addition, in indexcalculation, a value of “0” or “1” may be assigned to each point P_(k)*according to the position of the point P_(k)* and the above potentialinformation.

According to Embodiment 2, the same advantageous effects as those ofEmbodiment 1 can be achieved.

Moreover, a map is generated by setting a normal region on the featurespace. Therefore, the map information may include a mathematical formulaindicating the boundary of the normal region or a coefficientdetermining the mathematical formula, so that the information amount ofthe map information can be reduced.

Embodiment 3

Next, Embodiment 3 will be described with reference to FIG. 11 to FIG.13 . Embodiment 3 is different from Embodiment 1 and Embodiment 2 inthat a map is generated by using RRI information acquired not inpre-learning but in actual operation. FIG. 11 is a block diagram showinga biological signal processing device according to Embodiment 3, andFIG. 12 illustrates a map and an index calculation method according toEmbodiment 3. As shown in FIG. 11 , the RRI information acquisitionmeans 11 of a biological signal processing device 30 outputs the RRIinformation 822 acquired through “acquisition in actual operation” toboth an index calculation means 34 and a map generation means 32. Theindex calculation means 34 calculates the index 83 of the RRIinformation 822 by using a map. In addition, in Embodiment 3, a mapstorage means is not essential, and thus the biological signalprocessing device 30 does not include a map storage means. Hereinafter,a detailed description will be given.

(Description of Map Generation Method)

The map generation means 32 determines the coordinates of the pointsP_(k)* from the RRIs constituting the RRI information 822 acquired inactual operation, according to equation (2), plots each point P_(k)* onthe xy plane, and classifies each point P_(k)* by a clustering methodsuch as a self-organizing map or k-means method, to form a plurality ofpoint groups. In Embodiment 3, these point groups are referred to as“clusters”. In the example shown in FIG. 12 , three clusters (clustersCL, CL1, and CL2) are formed. Next, among the formed clusters, a clusterthat intersects a straight line L0 is set as a valid cluster, and acluster that does not intersect the straight line L0 is set as aninvalid cluster. In the example shown in FIG. 12 , the cluster CL is avalid cluster, and the clusters CL1 and CL2 are invalid clusters. InEmbodiment 3, one in which the valid cluster and the invalid clustersare generated on the xy plane which is a feature space is defined as a“map”. The map information 84 of Embodiment 3 may include thecoordinates of each point P_(k)* included in the cluster CL which is avalid cluster. This is because the points P_(k)* that are not includedin the cluster CL which is a valid cluster are included in the invalidclusters. The map generation means 32 outputs the map information 84 tothe index calculation means 34.

(Description of Index Calculation Method)

The index calculation means 34 assigns “1” to the points P_(k)* includedin the valid cluster, and assigns “0” to the other points P_(k)*, thatis, the points P_(k)* included in the invalid clusters, in the mapreproduced by using the map information 84. Then, the index calculationmeans 34 calculates the indexes 83 of the RRIs constituting each pointP_(k)*, in the same manner as in Embodiment 1. The index calculationmeans 34 outputs the index 83 of each RRI to the reliability calculationmeans 15.

Since the RRIs that determine the coordinates of each point P_(k)* arethe RRIs acquired in actual operation, there is a possibility that anabnormal value is also included. In Embodiment 3, the points P_(k)*included in the valid cluster are regarded as valid and “1” is assignedthereto, and the points P_(k)* included in the invalid clusters areregarded as invalid and “0” is assigned thereto, so that thediscrimination of whether each RRI is normal or abnormal is reflected inthe index 83 calculated therefrom.

The process after the index calculation is the same as in Embodiment 1.

The configuration of a watching system according to Embodiment 3 is aconfiguration in which, in the watching system according to Embodiment 1described with reference to FIG. 7A, the biological signal processingdevices 10A and 10B are replaced only by biological signal processingdevices 30A and 30B (not shown) having the same configuration as thebiological signal processing device 30, respectively. The otherconfiguration is the same, and thus the description thereof is omitted.The operation of the watching system, that is, a watching method,according to Embodiment 3, will be described below.

(Description of Watching Method)

FIG. 13 is a flowchart showing the operation of the watching systemaccording to Embodiment 3. In Embodiment 3, map generation is alsoperformed in actual operation, so that pre-learning is not performed.That is, step ST301 to step ST307 are an actual operation process.

First, RRI information of the target to be watched is acquired (stepST301). The RRI information acquisition means 11 of the biologicalsignal processing devices 30A and 30B acquires the RRI information 82Aand 82B periodically outputted from the sensors 80A and 80B which detectbiological signals of the targets to be watched. The RRI information 82Aand 82B acquired by the biological signal processing devices 30A and30B, respectively, are outputted as the RRI information 822 acquired inactual operation, to the index calculation means 34 and the mapgeneration means 32 of the biological signal processing devices 30A and30B.

Next, a map is generated from the RRI information acquired in step ST301(step ST302). The map generation means 32 of the biological signalprocessing devices 30A and 30B each plot the points P_(k)* on the xyplane by the value of each RRI constituting the RRI information 822acquired in actual operation, and classify each point P_(k)* asdescribed above, to form a plurality of clusters. In addition, thecluster CL which intersects the straight line L0 is set as a validcluster.

Next, the index of each RRI is calculated (step ST303). The indexcalculation means 34 assigns “1” to the points P_(k)* included in thevalid cluster, and assigns “0” to the points P_(k)* included in aninvalid cluster. Then, the indexes 83 of the RRIs constituting eachpoint P_(k)* are calculated in the same manner as in Embodiment 1. Theindex calculation means 34 outputs the index 83 of each RRI to thereliability calculation means 15.

Next, RRI reliability and correction RRI information are calculated(step ST304). The reliability calculation means 15 of the biologicalsignal processing devices 30A and 30B calculates the RRI reliability 85Aand 85B from the index 83 of each RRI, and outputs the RRI reliability85A and 85B to the RRI information correction means 16 of the biologicalsignal processing devices 30A and 30B and the watching server 70. TheRRI information correction means 16 corrects the RRI information 822 tobe evaluated, by using the RRI reliability 85A and 85B. A specificcorrection method is as described above. The RRI information correctionmeans 16 of the biological signal processing devices 30A and 30B outputsthe correction RRI information 86A and 86B to the watching server 70.

Next, the worn state of the sensor 80 is determined (step ST305). Thedetailed description thereof is the same as that of step ST106 inEmbodiment 1.

Next, the heart rate variability of the target to be watched is analyzed(step ST306). The detailed description thereof is the same as that ofstep ST107 in Embodiment 1.

Next, an analysis result is displayed (step ST307). The detaileddescription thereof is the same as that of step ST108 in Embodiment 1.

The others are the same as in Embodiment 1, and thus the descriptionthereof is omitted.

According to Embodiment 3, the reliability of the measurement state ofthe biological signal can be evaluated only from the RRI informationacquired in actual operation, without performing pre-learning. Morespecifically, the RRI information acquired in actual operation isoutputted to both the index calculation means and the map generationmeans without being divided into the RRI information in the normal stateserving as a basis and the RRI information to be evaluated. The mapgeneration means plots points on the xy plane, which is a feature space,on the basis of each RRI constituting the acquired RRI information, thenclassifies the plotted points into a plurality of clusters byclustering, and determines a valid cluster and an invalid cluster. Thereis a possibility that the RRIs acquired in actual operation includeabnormal ones. However, a valid cluster is determined from among aplurality of clusters, different values are assigned on the basis ofwhether or not the points composed of the RRIs to be evaluated areincluded in the valid cluster, and the index of each RRI is calculatedon the basis of this value, so that the acquired RRI information can beevaluated. In Embodiments 1 and 2, as a result of performingpre-learning, the state of the target to be watched can be analyzed inreal time in actual operation. On the other hand, real-time analysis isnot required for a purpose in which it is not necessary to monitor thestate of the target to be watched in real time and the state is analyzedlater, for example. According to Embodiment 3, the RRI information,collection of which is completed in actual operation, can be analyzedoffline and can be separated into a section in which normal measurementhas been successfully performed and a section in which normalmeasurement has not been successfully performed.

When a map storage means is provided in the biological signal processingdevice of Embodiment 3, the state of the target to be watched can bemonitored in real time by storing map information of a map generated infirst offline analysis, in the map storage means, and reproducing themap by using the map information stored in the map storage means, in thenext analysis or later analysis.

Embodiment 4

Next, Embodiment 4 will be described with reference to FIG. 14 and FIG.15 . Embodiment 4 is different from Embodiment 3 in a method forgenerating a map for evaluating the reliability of RRI information and amethod for calculating the index of an RRI. FIG. 14 illustrates a mapand an index calculation method according to Embodiment 4, and FIG. 15shows the relationship between an angle θ*, which is the vertex angle ofan isosceles triangle indicating a normal region according to Embodiment4, and the number of points P_(k)* inside the normal region. InEmbodiment 4, each point P_(k)* is plotted on the xy plane on the basisof the RRIs acquired in actual operation. In addition, the normal regionis determined on the basis of the variation of the points P_(k)* withinthe normal region when the normal region is expanded, and the index ofeach RRI is calculated. Hereinafter, a detailed description will begiven.

(Description of Map Generation Method)

The map generation means 32 determines the coordinates of the pointsP_(k)* from the RRIs constituting the RRI information 822 acquired inactual operation, according to equation (2), and plots the points P_(k)*on the xy plane. Here, it is assumed that N RRIs are acquired (N−1points P_(k)* are plotted). The map generation means 32 classifies eachpoint P_(k)* in the same manner as in Embodiment 3, to form a pluralityof clusters, that is, point groups. In the example shown in FIG. 14 ,three clusters (clusters CL, CL1, and CL2) are formed. Next, among theformed clusters, a cluster that intersects a straight line L0 is set asa valid cluster, and a cluster that does not intersect the straight lineL0 is set as an invalid cluster. Next, with a straight line L0 (y=x) asan axis of symmetry, two straight lines, a straight line L1* and astraight line L2* which intersect the straight line L0 at anintersection point C0* and whose angles with respect to the straightline L0 is θ*, are drawn. Furthermore, a straight line L3* which has agradient of −1 and which intersects the straight line L1* and thestraight line L2* at an intersection point C1* and an intersection pointC2*, respectively, is drawn. A region surrounded by the straight lineL1*, the straight line L2*, and the straight line L3* is set as a normalregion S*. The position of the intersection point C0* and the initialpositions (positions at θ*=0) of the intersection points C1* and C2* aredetermined in advance on the basis of the physiological findings.

The map generation means 32 makes θ* variable and gradually increasesthe value of θ* with an initial value as 0. In addition, the mapgeneration means 32 counts the number of points P_(k)* inside the normalregion S* while increasing the value of θ*. As shown in FIG. 15 , whenthe value of θ* is increased from 0, the number of points P_(k)* insidethe normal region S* increases, but the increase in the number of pointsP_(k)* becomes almost zero at a certain value θth. This means that theboundary of the normal region S* has been reached between the cluster CLwhich is a valid cluster and the clusters CL1 and CL2 which are invalidclusters. The map generation means 32 determines θth as a boundaryvalue, and sets the normal region S*. The set normal region S*corresponds to the case where θ*=θth in FIG. 14 , and all the pointsP_(k)* constituting the cluster CL which is a valid cluster are placedinside the normal region S* or on the boundary of the normal region S*.Therefore, the setting of the normal region S* is also the setting ofthe boundary between the valid cluster and each invalid cluster. InEmbodiment 4, one in which the normal region S* is set on the xy planewhich is a feature space is defined as a “map”.

Since the normal region S* is determined by the straight lines L1*, L2*,and L3* when θ*=θth, the mathematical formulas indicating these straightlines are the map information 84 in Embodiment 4. In addition, since thenormal region S* is also determined by θth and the intersection pointsC0*, C1*, and C2*, θth and the intersection points C0*, C1*, and C2* maybe the map information 84. That is, similar to Embodiment 2, the mapinformation of Embodiment 4 also may include a mathematical formulaindicating the boundary of the normal region S* which is the map, or acoefficient determining the mathematical formula.

In the example shown in FIG. 14 , the shape of the normal region S* isan isosceles triangle, but similar to the normal region S of Embodiment2, the geometric shape of the normal region S* is not limited. Inaddition, the normal region S* can also be determined even in the casewhere the feature space is three-dimensional.

If there are many abnormal values, there is a possibility that the validcluster and the invalid cluster cannot be necessarily separated fromeach other. In this case, even when θ* is increased, the number ofpoints P_(k)* inside the normal region S* continues to increasepermanently, so that the normal region S* cannot be determined. It isconsidered that, in view of such a possibility, abnormality detectionindicating that there are too many abnormal values in the acquired RRIswhen θ* reaches a value having a predetermined magnitude is performed.It is considered that when abnormality detection is performed in mapgeneration, the RRI information 82 is acquired again and map generationis performed again.

(Description of Index Calculation Method)

The index calculation means 14 compares each point P_(k)* with the mapinformation 84, assigns “1” to the points P_(k)* that are within thedetermined normal region S*, and assigns “0” to the points P_(k)* thatare outside the determined normal region S*. Then, the index calculationmeans 14 calculates the indexes 83 of the RRIs constituting each pointP_(k)*, in the same manner as in Embodiment 2. The index calculationmeans 14 outputs the index 83 of each RRI to the reliability calculationmeans 15.

The others are the same as in Embodiment 3, and thus the descriptionthereof is omitted.

In Embodiment 4 as well, similar to Embodiment 2, potential informationin which “1” is set for the inside of the normal region S* and “0” isset for the outside of the normal region S* is set in map generation,and one obtained by adding the potential information to the featurespace may be used as a map. In this case, the map information 84includes the above potential information. In addition, in indexcalculation, a value of “0” or “1” may be assigned to each point P_(k)*according to the position of the point P_(k)* and the above potentialinformation.

According to Embodiment 4, the same advantageous effects as those ofEmbodiment 3 can be achieved.

Although the disclosure is described above in terms of various exemplaryembodiments and implementations, it should be understood that thevarious features, aspects, and functionality described in one or more ofthe individual embodiments are not limited in their applicability to theparticular embodiment with which they are described, but instead can beapplied, alone or in various combinations to one or more of theembodiments of the disclosure.

It is therefore understood that numerous modifications which have notbeen exemplified can be devised without departing from the scope of thepresent disclosure. For example, at least one of the constituentcomponents may be modified, added, or eliminated. At least one of theconstituent components mentioned in at least one of the preferredembodiments may be selected and combined with the constituent componentsmentioned in another preferred embodiment.

DESCRIPTION OF THE REFERENCE CHARACTERS

-   -   10, 10A, 10B, 30 biological signal processing device    -   11 RRI information acquisition means    -   12, 32 map generation means    -   13 map storage means    -   14, 34 index calculation means    -   15 reliability calculation means    -   16 RRI information correction means    -   70, 701 watching server    -   715 biological signal processing means    -   72 heart rate variability analysis means    -   73 analysis result output means    -   80, 80A, 80B sensor    -   82, 82A, 82B, 821, 822 RRI information    -   83 index    -   84 map information    -   85, 85A, 85B RRI reliability    -   86, 86A, 86B correction RRI information    -   100, 101 watching system    -   CL, CL1, CL2 cluster    -   E analysis result    -   L0 straight line    -   P point group    -   P_(i), P_(k)* point    -   S, S* normal region

1. A biological signal processing device comprising: a processor for executing a program; and a memory or a hard disk for storing the program, wherein the following operation is performed by the program executed by the processor, acquiring, from a sensor for calculating RRIs of a biological signal of a target to be watched, RRI information composed of a plurality of the RRIs arranged in time series; plotting a plurality of first points whose positions are determined on the basis of values of the RRIs in a normal state among the RRIs constituting the RRI information, on a feature space, and generating a map from the feature space on the basis of the plurality of first points; plotting a second point whose position is determined by a value of each RRI to be evaluated, on the feature space, and calculating an index indicating resemblance to heart rate variability of each RRI to be evaluated, on the basis of a relationship between the second point and the map; and calculating RRI reliability indicating reliability of each RRI to be evaluated, from the index.
 2. The biological signal processing device according to claim 1, wherein, the processor corrects RRI information on the basis of the RRI reliability.
 3. The biological signal processing device according to claim 2, wherein the processor corrects the RRI information by invalidating the RRI whose RRI reliability is smaller than a predetermined threshold value, and deleting the invalidated RRI from the RRI information.
 4. The biological signal processing device according to claim 3, wherein, when the RRI reliability consecutively falls below the threshold value a predetermined number of times, the processor invalidates the RRIs whose RRI reliability falls below the threshold value.
 5. The biological signal processing device according to claim 1, wherein the memory or the hard disk stores map information for reproducing the map from the feature space, wherein the processor acquires the RRI information including the RRIs in the normal state, through pre-learning, generates the map by determining the positions of the first points on the basis of the values of the RRIs in the normal state acquired through the pre-learning, and outputs the map information of the generated map to the memory or the hard disk.
 6. The biological signal processing device according to claim 5, wherein the map information includes coordinates of each point constituting a point group which intersects a straight line indicating a state where the RRIs are constant within the feature space, and the processor calculates the index on the basis of a degree of deviation between the point group and the second point.
 7. The biological signal processing device according to claim 5, wherein the map information includes information of a mathematical formula representing a boundary of a normal region, a point group which intersects a straight line indicating a state where the RRIs are constant within the feature space is located on the boundary of or inside the normal region, and the processor calculates the index on the basis of whether or not the second point is on the boundary of or inside the normal region.
 8. The biological signal processing device according to claim 7, wherein the feature space is a two-dimensional plane, and the normal region has an isosceles triangle shape with the straight line as an axis of symmetry.
 9. The biological signal processing device according to claim 1, wherein the processor acquires the RRI information including the RRIs in the normal state, in actual operation, and generates the map by determining the positions of the first points on the basis of the values of the RRIs in the normal state acquired in the actual operation.
 10. The biological signal processing device according to claim 9, wherein the first points constitute a point group which intersects a straight line indicating a state where the RRIs are constant within the feature space, and the processor calculates the index on the basis of whether or not the second point is included in the point group.
 11. The biological signal processing device according to claim 9, wherein the first points constitute a point group which intersects a straight line indicating a state where the RRIs are constant within the feature space, and the processor calculates the index on the basis of whether or not the second point is on a boundary of or inside a normal region, the point group is located on the boundary of or inside the normal region.
 12. The biological signal processing device according to claim 11, wherein the feature space is a two-dimensional plane, and the normal region has an isosceles triangle shape with the straight line as an axis of symmetry.
 13. The biological signal processing device according to claim 9, wherein the memory or the hard disk stores map information for reproducing the map from the feature space, wherein the processor outputs the map information of the generated map to the memory or the hard disk.
 14. A watching system comprising: a sensor for calculating RRIs of a biological signal of a target to be watched; a processor for executing a program; and a memory or a hard disk for storing the program, wherein the following operation is performed by the program executed by the processor, acquiring RRI information composed of a plurality of the RRIs arranged in time series; plotting a plurality of first points whose positions are determined on the basis of values of the RRIs in a normal state among the RRIs constituting the RRI information, on a feature space, and generating a map from the feature space on the basis of the plurality of first points; plotting a second point whose position is determined by a value of each RRI to be evaluated, on the feature space, and calculating an index indicating resemblance to heart rate variability of each RRI to be evaluated, on the basis of a relationship between the second point and the map; calculating RRI reliability indicating reliability of each RRI to be evaluated, from the index; correcting the RRI information on the basis of the RRI reliability and outputting the RRI information as correction RRI information; analyzing heart rate variability of the target to be watched, on the basis of the correction RRI information; and outputting an analysis result by analyzing the heart rate variability.
 15. The watching system according to claim 14, wherein the processor evaluates a worn state of the sensor worn by the target to be watched, on the basis of the RRI reliability.
 16. A watching method comprising: a step of acquiring RRIs of a biological signal of a target to be watched, by a sensor, and acquiring RRI information composed of a plurality of the RRIs arranged in time series; a step of plotting a plurality of first points whose positions are determined on the basis of values of the RRIs in a normal state among the plurality of the RRIs, on a feature space, and generating a map from the feature space on the basis of the plurality of first points; a step of plotting a second point whose position is determined by a value of each RRI to be evaluated, on the feature space, and calculating an index indicating resemblance to heart rate variability of each RRI to be evaluated, on the basis of a relationship between the second point and the map; a step of calculating RRI reliability indicating reliability of each RRI to be evaluated, from the index; a step of correcting the RRI information on the basis of the RRI reliability to obtain correction RRI information; a heart rate variability analysis step of analyzing heart rate variability of the target to be watched, on the basis of the correction RRI information; and an analysis result output step of outputting an analysis result by the heart rate variability analysis step.
 17. The watching method according to claim 16, wherein a worn state of the sensor worn by the target to be watched is evaluated on the basis of the RRI reliability. 